The present invention relates to a facility state monitoring method of early detecting an anomaly on the basis of multidimensional time series data output from a plant, a facility and so forth and a device for same.
A power company supplies district heating hot water and supplies high pressure steam and low pressure steam to factories by utilizing waste heat and so forth of a gas turbine. A petrochemical company operates the gas turbine and so forth as a power supply facility. In various plants and facilities using the gas turbines and so forth as described above, preventive maintenance that a malfunction of a facility or a symptom thereof is detected is remarkably important also in order to minimize the damage to society.
Facilities which require such preventive maintenance as mentioned above as for degradation, life and so forth of batteries loaded thereon are too numerous to enumerate such as not only gas turbines and steam turbines but also water wheels in hydraulic power plants, nuclear reactors of nuclear power plants, windmills of wind farms, engines of airplanes and heavy machines, railroad vehicles and railroad tracks, escalators, elevators, and also equipment and component levels.
Therefore, it is conducted to attach a plurality of sensors to object facilities and plants so as to determine whether they are normal or anomaly in accordance with a monitoring standard for each sensor. In U.S. Pat. No. 6,952,662 (Patent Document 1) and U.S. Pat. No. 6,975,962 (Patent Document 2), anomaly detecting methods mainly targeted at engines are disclosed. This is the one that past data, for example, time series sensor signals are held as database, the degree of similarity between observation data and past leaning data is calculated by an original method, an estimated value is calculated by linear combination of pieces of data which are high in degree of similarity, and the degree of difference between the estimated value and the observation data is output. In addition, in Japanese Patent Application laid-Open No. 2010-191556 (Patent Document 3), an anomaly detecting method of extracting a compact learning data set which is similar to observation data from past normal data, modeling the extracted learning data in a sub-space, and detecting an anomaly on the basis of a distance between the observation data and the sub-space is disclosed.